PROJECT SUMMARY/ABSTRACT The objective of this training and research project is to develop the candidate into an independent and interdisciplinary ELSI researcher, with the ability to intertwine qualitative empirical with normative approaches to issues in genetics and genomics. To accomplish this objective, I have proposed: (1) Training that builds upon my previous education and experience as a qualitative social scientist with additional training in genetics and bioethics; and (2) Research that takes a combined social scientific and bioethical approach to understanding and guiding the translational pathways of new genetic innovations. The proposed training includes coursework, directed readings, seminars and workshops, and the mentored development of an interdisciplinary ELSI research project. The long-term goal of the research project is to map out an ELSI-integrated translational pathway for genetic innovations: building a model of crucial points for, and types of, ELSI guidance throughout these translational processes. The specific Aims of this project are: (1) To follow the unfolding translational pathway of cffDNA testing technology, as a case study in translation processes for genetic innovations; and (2) To map crucial points for ELSI guidance along the translational pathway of genetic innovations. Through individual interviews with stakeholders and observations of meetings and conferences of stakeholders, this mapping project will examine the case of cell-free fetal DNA (cffDNA) testing and the actors and networks that influence its development toward potential clinical applications. Comparing features of this process with those of other translations of genetic innovations, it will identify critical moments, turning points, and intersections at which important decisions shape the unfolding translational pathway. Finally, through collaboration with key stakeholders in cffDNA and non-invasive prenatal testing, a model will be developed that proposes types of ethical and social guidance for these crucial points that would likely be most beneficial.